Model save path: ./New_Models/bn_True_dataset_CIFAR100_epochs_200_lr_0.01_model_type_vgg11_rand_seed_265358_test_samples_None_train_samples_None_weight_decay_0.003.pth.tar
Training Set:
Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.022893192246556282
Inter Cos: 0.07011394202709198
Norm Quadratic Average: 29.326324462890625
Nearest Class Center Accuracy: 0.03258

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.026122380048036575
Inter Cos: 0.02930012345314026
Norm Quadratic Average: 8.39084529876709
Nearest Class Center Accuracy: 0.04762

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.0215773805975914
Inter Cos: 0.023408053442835808
Norm Quadratic Average: 4.460314750671387
Nearest Class Center Accuracy: 0.06058

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.017662152647972107
Inter Cos: 0.018173275515437126
Norm Quadratic Average: 3.3323535919189453
Nearest Class Center Accuracy: 0.06938

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.022659040987491608
Inter Cos: 0.01867072656750679
Norm Quadratic Average: 2.262173652648926
Nearest Class Center Accuracy: 0.08016

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.025242382660508156
Inter Cos: 0.021050792187452316
Norm Quadratic Average: 1.528173565864563
Nearest Class Center Accuracy: 0.08866

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.08090175688266754
Inter Cos: 0.04951101914048195
Norm Quadratic Average: 1.0529106855392456
Nearest Class Center Accuracy: 0.0995

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.4971456527709961
Inter Cos: 0.14214739203453064
Norm Quadratic Average: 1.1148213148117065
Nearest Class Center Accuracy: 0.09998

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 4.494444370269775
Linear Weight Rank: 511
Intra Cos: 0.8966261744499207
Inter Cos: 0.256600022315979
Norm Quadratic Average: 41.09260940551758
Nearest Class Center Accuracy: 0.1

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 4.72005558013916
Linear Weight Rank: 1585
Intra Cos: 0.9357502460479736
Inter Cos: 0.31303444504737854
Norm Quadratic Average: 31.980831146240234
Nearest Class Center Accuracy: 0.1

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 4.813281536102295
Linear Weight Rank: 97
Intra Cos: 0.9359225630760193
Inter Cos: 0.31978973746299744
Norm Quadratic Average: 29.708696365356445
Nearest Class Center Accuracy: 0.1

Output Layer:
Intra Cos: 0.9421936869621277
Inter Cos: 0.3468267023563385
Norm Quadratic Average: 30.404939651489258
Nearest Class Center Accuracy: 0.1

Test Set:
Average Loss: 1.7438150161743164
Accuracy: 0.5875
NC1 Within Class Collapse: nan
NC2 Equinorm: Features: 0.23960907757282257, Weights: 0.01494806818664074
NC2 Equiangle: Features: 0.1962863621567235, Weights: 0.14438414910827022
NC3 Self-Duality: 0.24768218398094177
NC4 NCC Mismatch: 0.15559999999999996

Layer 0: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.006621266715228558
Inter Cos: 0.4067547917366028
Norm Quadratic Average: 29.42218589782715
Nearest Class Center Accuracy: 0.1136

Layer 1: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.011042161844670773
Inter Cos: 0.24754008650779724
Norm Quadratic Average: 8.448773384094238
Nearest Class Center Accuracy: 0.2587

Layer 2: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.015151665546000004
Inter Cos: 0.20294997096061707
Norm Quadratic Average: 4.493091583251953
Nearest Class Center Accuracy: 0.3991

Layer 3: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.01346914004534483
Inter Cos: 0.1406272053718567
Norm Quadratic Average: 3.347961664199829
Nearest Class Center Accuracy: 0.5169

Layer 4: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.012401741929352283
Inter Cos: 0.14448018372058868
Norm Quadratic Average: 2.267909049987793
Nearest Class Center Accuracy: 0.6212

Layer 5: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.010217520408332348
Inter Cos: 0.1331549435853958
Norm Quadratic Average: 1.5197187662124634
Nearest Class Center Accuracy: 0.6888

Layer 6: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.020123329013586044
Inter Cos: 0.19718146324157715
Norm Quadratic Average: 1.0170663595199585
Nearest Class Center Accuracy: 0.6704

Layer 7: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
Intra Cos: 0.06207190826535225
Inter Cos: 0.43369704484939575
Norm Quadratic Average: 0.9419509172439575
Nearest Class Center Accuracy: 0.603

Layer 8: Linear(in_features=25088, out_features=4096, bias=True)
Linear Weight Norm: 4.494444370269775
Linear Weight Rank: 511
Intra Cos: 0.2019672393798828
Inter Cos: 0.5019068121910095
Norm Quadratic Average: 31.130008697509766
Nearest Class Center Accuracy: 0.5809

Layer 9: Linear(in_features=4096, out_features=4096, bias=True)
Linear Weight Norm: 4.72005558013916
Linear Weight Rank: 1585
Intra Cos: 0.2242664396762848
Inter Cos: 0.5266466736793518
Norm Quadratic Average: 24.456945419311523
Nearest Class Center Accuracy: 0.5797

Layer 10: Linear(in_features=4096, out_features=100, bias=True)
Linear Weight Norm: 4.813281536102295
Linear Weight Rank: 97
Intra Cos: 0.21461129188537598
Inter Cos: 0.5264964699745178
Norm Quadratic Average: 23.077341079711914
Nearest Class Center Accuracy: 0.5802

Output Layer:
Intra Cos: 0.22044256329536438
Inter Cos: 0.5483376979827881
Norm Quadratic Average: 23.471927642822266
Nearest Class Center Accuracy: 0.577

